code: def extract_features(self, x, padding_mask=None, tgt_layer=None):
      if padding_mask is not None:
          x[padding_mask] = 0
  
      x_conv = self.pos_conv(x.transpose(1, 2))
      x_conv = x_conv.transpose(1, 2)
      x = x + x_conv
  
      if not self.layer_norm_first:
          x = self.layer_norm(x)
  
      x = F.dropout(x, p=self.dropout, training=self.training)
  
      # B x T x C -> T x B x C
      x = x.transpose(0, 1)
  
      layer_results = []
      z = None
      if tgt_layer is not None:
          layer_results.append((x, z))
      r = None
      pos_bias = None
      for i, layer in enumerate(self.layers):
          if self.layer_wise_gradient_decay_ratio != 1.0:
              x = GradMultiply.apply(
                  x, self.layer_wise_gradient_decay_ratio
              )
          dropout_probability = np.random.random()
          if not self.training or (
              dropout_probability > self.layerdrop
          ):
              x, z, pos_bias = layer(
                  x,
                  self_attn_padding_mask=padding_mask,
                  need_weights=False,
                  pos_bias=pos_bias,
              )
          if tgt_layer is not None:
              layer_results.append((x, z))
          if i == tgt_layer:
              r = x
              break
  
      if r is not None:
          x = r
  
      # T x B x C -> B x T x C
      x = x.transpose(0, 1)
  
      return x, layer_results
  
imports: import math
  import numpy
  from typing import Dict, Optional, Tuple
  import torch
  from torch import Tensor, nn
  import torch.nn.functional
  from torch.nn import LayerNorm, Parameter
  from lavis.models.beats.modules import GradMultiply, SamePad, get_activation_fn, GLU_Linear, quant_noise
label_desc: Use image node_id 3 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.extract_features(x, padding_mask, tgt_layer) and returns: x,  layer_results